Answers
What are embeddings?
Embeddings are numeric vectors that represent the meaning of text, images, or audio, produced by a trained model. Items with similar meaning end up close together in vector space, even when the original wording differs. Engineering teams use embeddings for semantic search, clustering, recommendation, and retrieval feeding language model prompts.
01How are embeddings created?
An embedding model, trained on large amounts of text or images, converts raw input into a fixed length list of numbers, often several hundred to a few thousand dimensions. During training, the model learns to place related concepts near each other in that numeric space, so a sentence about refunds sits close to a sentence about returns.
02What can embeddings be used for?
Embeddings support semantic search, where a query finds relevant documents by meaning rather than exact keywords. Embeddings also power recommendation engines, duplicate detection, clustering for topic discovery, and retrieval augmented generation, where relevant passages get pulled into a prompt before a language model generates an answer.
03How do embeddings differ from keywords?
Keyword matching compares literal words and misses synonyms or paraphrases, while an embedding compares underlying meaning across a continuous vector space. A keyword search for laptop battery life might miss a document about notebook power duration, but an embedding based search can surface it because the concepts map to nearby vectors.
04Where are embeddings stored and searched?
Embeddings typically get stored in a vector database or a vector index attached to an existing database, built for fast nearest neighbor lookup across large collections. Storage choice depends on data volume, latency requirements, and whether metadata filtering must run alongside the similarity search.
05What limits the quality of embeddings?
Embedding quality depends on the training data and domain fit of the chosen model, chunk size when splitting long documents, and consistency between the model used at indexing time and the model used at query time. A mismatch between those two models produces vectors that cannot be compared meaningfully, which quietly breaks search relevance.
06Specification
| Spec | Detail |
|---|---|
| Typical cost | Generating embeddings usually costs fractions of a cent per document through hosted APIs, or compute time when self hosting an open model. |
| Typical timeline | Embedding an existing document set typically takes a few hours to a few days depending on volume and chunking design. |
| Prerequisites | Clean source content, a chosen embedding model, and a chunking strategy for long documents. |
| Common failure modes | Model mismatch between indexing and querying, oversized chunks, and stale embeddings after source content changes. |
| Who it suits | Teams building semantic search, recommendation features, or retrieval for an AI assistant. |
07Embedding use cases
| Use case | What embeddings enable |
|---|---|
| Semantic search | Matching by meaning instead of exact keywords |
| Retrieval augmented generation | Pulling relevant passages into a prompt |
| Clustering | Grouping similar documents for topic discovery |
| Recommendation | Surfacing items similar to a user's past behavior |
FAQQuestions
Related questions
Are embeddings the same for every model?
No. Each embedding model produces vectors in its own coordinate space, so vectors from one model cannot be compared directly against vectors from another model.
Do embeddings expose the original text?
Embeddings do not directly contain readable text, but research has shown that some information can be partially reconstructed, so sensitive content still needs standard access controls.
How many dimensions does an embedding have?
Common embedding models produce vectors ranging from around 300 to several thousand dimensions, with the exact count fixed by the chosen model.
Do embeddings need to be updated?
Yes. When source documents change or the embedding model gets upgraded, existing embeddings need to be regenerated to stay accurate and comparable.
NEXTWhere to go next
AI integration is the Lingows AI page that covers this work in delivery detail, including scope, timeline, and how the build is run.
Last reviewed 2026-08-16